Quantitative Metabolite Profiling Utilizing Parallel Column Analysis for Simultaneous Reversed-Phase and Hydrophilic Interaction Liquid Chromatography Separations Combined with Tandem Mass Spectrometry

In this work, a fully automated parallel LC column method was established in order to perform orthogonal hydrophilic interaction chromatography (HILIC) and reversed-phase (RPLC) chromatography within one analytical run for targeted quantitative mass spectrometric determination of metabolites from central carbon metabolism. In this way, the analytical throughput could be significantly improved compared to previously established dual separation work flows involving two separate analytical runs. Two sample aliquots were simultaneously injected onto a dual column setup columns using a ten-port valve, and parallel separations were carried out. Sub 2 μm particle size stationary phases were employed for both separation methods. HILIC and RPLC eluents were combined post column followed by ESI-MS/MS detection. The orthogonal separations were optimized, aiming at an overall separation with 2 retention time segments, while reversed-phase separation was accomplished within 5.5 min; metabolites on the HILIC phase were retained for a minimum time of 6 min. The overall run time was 15 min. The setup was applied to the quantification of 30 primary intercellular metabolites, including amino acids, organic acids, and nucleotides employing internal standardization by a fully ¹³C-labeled yeast extract. The comparison with HILIC–MS/MS and RPLC–MS/MS in separate analytical runs revealed that an excellent analytical performance was achieved by the parallel LC column method. The experimental repeatability (<i>N</i> = 5) was on average <5% (only for 2 compounds >5%). Moreover, limits of detection for the new approach ranging from 0.002–15 μM were in a good agreement with ones obtained in separate HILIC–MS/MS and RPLC–MS/MS runs (ranging from 0.01–44 μM)

Table_1_Exploring disease-specific metabolite signatures in hereditary angioedema patients.xlsx

IntroductionHereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients’ metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE).MethodsBlood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model.ResultsA total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE.ConclusionOur study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care.</p

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

<div><p>Objective</p><p>Our basic understanding of ascending thoracic aortic aneurysm (ATAA) pathogenesis is still very limited, hampering early diagnosis, risk prediction, and development of treatment options. “Omics”-technologies, ideal to reveal tissue alterations from the normal physiological state due to disease have hardly been applied in the field. Using a metabolomic approach, with this study the authors seek to define tissue differences between controls and various forms of ATAAs.</p><p>Methods</p><p>Using a targeted FIA-MS/MS metabolomics approach, we analysed and compared the metabolic profiles of ascending thoracic aortic wall tissue of age-matched controls (n = 8), bicuspid aortic valve-associated aneurysms (BAV-A; n = 9), tricuspid aortic valve-associated aneurysms (TAV-A; n = 14), and tricuspid aortic valve-associated aortic dissections (TAV-Diss; n = 6).</p><p>Results</p><p>With sphingomyelin (SM) (OH) C22:2, SM C18:1, SM C22:1, and SM C24:1 only 4 out of 92 detectable metabolites differed significantly between controls and BAV-A samples. Between controls and TAV-Diss samples only phosphatidylcholine (PC) ae C32:1 differed. Importantly, our analyses revealed a general increase in the amount of total sphingomyelin levels in BAV-A and TAV-Diss samples compared to controls.</p><p>Conclusions</p><p>Significantly increased levels of sphingomyelins in BAV-A and TAV-Diss samples compared to controls may argue for a repression of sphingomyelinase activity and the sphingomyelinase-ceramide pathway, which may result in an inhibition of tissue regeneration; a potential basis for disease initiation and progression.</p></div

Total metabolite class concentrations in controls and different ATAA samples.

<p>Fig 4 shows the disease group-specific concentration of the sum of metabolites per metabolite class. The sums given include only those metabolites per class that were included in the analysis (for details see Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176727#pone.0176727.s001" target="_blank">S1 File</a>). C, control (n = 8); BAV-A, bicuspid aortic valve-associated aneurysm (n = 9); TAV-A, tricuspid aortic valve-associated aneurysm (n = 14); TAV-Diss, tricuspid aortic valve-associated dissection (n = 6). Dots represent median values per patient sample; black line indicates the groups mean value. Asterisks indicate significant differences between groups (ANOVA, Bonferroni adjusted) *, p<0.05.</p

Differences in metabolites and metabolite compound classes between controls and ATAA forms.

<p>In the left side of Fig 2 the numbers near the lines which connect group symbols indicate the number of metabolites which differ significantly between groups. C, control (n = 8); BAV-A, bicuspid aortic valve-associated aneurysm (n = 9); TAV-A, tricuspid aortic valve-associated aneurysm (n = 14); TAV-Diss, tricuspid aortic valve-associated dissection (n = 6). Using the letters (a–f) superscripted to the numbers on the left side of Fig 2, metabolites which differ between groups can be assigned to compound classes in the table on the right side of the Figure. Significant differences (p<0.05) in individual metabolite concentration between groups were determined by ANOVA and multiple two-sided t-test comparisons (non-adjusted for maximum sensitivity).</p

Aortic sample donor characteristics.

<p>Aortic sample donor characteristics.</p

Metabolites with significant differences between controls and ATAA groups.

<p>Fig 3 shows single metabolites with significant differences in tissue concentration between groups (ANOVA, and multiple two-sided t-test comparisons, Bonferroni adjusted). C, control (n = 8); BAV-A, bicuspid aortic valve-associated aneurysm (n = 9); TAV-A, tricuspid aortic valve-associated aneurysm (n = 14); TAV-Diss, tricuspid aortic valve-associated dissection (n = 6). *, p<0.05. SM: sphingomyelin; PC ae: phosphatidylcholine acyl-alkyl.</p

Hierarchical cluster analysis of total sphingomyelin concentrations.

<p>Fig 5 shows the distribution of sphingomyelin concentrations per patient tissue sample by hierarchical cluster analysis. Metabolite identity is indicted on the right side. Colours indicate compound concentrations. Colour scale bar (low concentration (dark blue) to high concentration (dark red) and group colour code are given in the upper right. C, control; BAV-A, bicuspid aortic valve-associated thoracic aneurysm; TAV-A, tricuspid aortic valve-associated thoracic aneurysm; TAV-Diss, tricuspid aortic valve-associated thoracic dissection; SM, sphingomyelin.</p

Volcano blot analyses reveal significant shifts in metabolite concentrations between controls and ATAA groups.

<p>The volcano blots shown in Fig 1 depict the differences between metabolite concentrations between the two groups indicated at the top of each blot. Each dot represents a single metabolite (out of the 92 detectable). On the x-axis the fold change is given (i.e. value (ratio) of the group named second, when group named first is set to one (dashed vertical line)).Values above p = 0.05 (dashed horizontal line) are considered to differ significantly between the two groups. C, control; BAV-A, bicuspid aortic valve-associated thoracic aneurysm; TAV-A, tricuspid aortic valve-associated thoracic aneurysm; TAV-Diss, tricuspid aortic valve-associated thoracic dissection.</p

Additional file 3: of Systems-level organization of yeast methylotrophic lifestyle

Enrichment of the peroxisomal marker protein Pex3p in the peroxisomal fraction. (PDF 271 kb